Vehicle Detection

The goals / steps of this project are the following:

  • Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
  • Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
  • Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
  • Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
  • Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
  • Estimate a bounding box for vehicles detected.

Import Statements

In [1]:
from skimage.feature import hog
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
# for scikit-learn >= 0.18 use:
from sklearn.model_selection import train_test_split
# from sklearn.cross_validation import train_test_split
from scipy.ndimage.measurements import label
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import numpy as np
import pickle
import cv2
import glob
import time

%matplotlib inline

Load Training Data

In [2]:
car_images = glob.glob('training_dataset/vehicles/**/*.png')
noncar_images = glob.glob('training_dataset/non-vehicles/**/*.png')
print(len(car_images), len(noncar_images))
8792 8968

Visualize Data

In [3]:
fig, axs = plt.subplots(4,4, figsize=(16, 16))
fig.subplots_adjust(hspace = .2, wspace=.001)
axs = axs.ravel()

for i in np.arange(8):
    img = cv2.imread(car_images[np.random.randint(0,len(car_images))])
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    axs[i].axis('off')
    axs[i].set_title('car', fontsize=10)
    axs[i].imshow(img)
for i in np.arange(8,16):
    img = cv2.imread(noncar_images[np.random.randint(0,len(noncar_images))])
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    axs[i].axis('off')
    axs[i].set_title('not car', fontsize=10)
    axs[i].imshow(img)

Convert image to Histogram of oriented gradients (HOG)

In [4]:
def get_hog_features(img, orient, pix_per_cell, cell_per_block, 
                        vis=False, feature_vec=True):
    # Call with two outputs if vis==True
    if vis == True:
        features, hog_image = hog(img, orientations=orient, 
                                  pixels_per_cell=(pix_per_cell, pix_per_cell),
                                  cells_per_block=(cell_per_block, cell_per_block), 
                                  transform_sqrt=False, 
                                  visualise=vis, feature_vector=feature_vec)
        return features, hog_image
    # Otherwise call with one output
    else:      
        features = hog(img, orientations=orient, 
                       pixels_per_cell=(pix_per_cell, pix_per_cell),
                       cells_per_block=(cell_per_block, cell_per_block), 
                       transform_sqrt=False, 
                       visualise=vis, feature_vector=feature_vec)
        return features
    
car_img = mpimg.imread(car_images[5])
_, car_dst = get_hog_features(car_img[:,:,2], 9, 8, 8, vis=True, feature_vec=True)
noncar_img = mpimg.imread(noncar_images[5])
_, noncar_dst = get_hog_features(noncar_img[:,:,2], 9, 8, 8, vis=True, feature_vec=True)

# Visualize 
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(7,7))
f.subplots_adjust(hspace = .4, wspace=.2)
ax1.imshow(car_img)
ax1.set_title('Car Image', fontsize=16)
ax2.imshow(car_dst, cmap='gray')
ax2.set_title('Car HOG', fontsize=16)
ax3.imshow(noncar_img)
ax3.set_title('Non-Car Image', fontsize=16)
ax4.imshow(noncar_dst, cmap='gray')
ax4.set_title('Non-Car HOG', fontsize=16)
Out[4]:
<matplotlib.text.Text at 0x11bda4358>

Method to Extract HOG Features from an Array of Car and Non-Car Images

In [5]:
# Define a function to extract features from a list of image locations
# This function could also be used to call bin_spatial() and color_hist() (as in the lessons) to extract
# flattened spatial color features and color histogram features and combine them all (making use of StandardScaler)
# to be used together for classification
def extract_features(imgs, cspace='RGB', orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0):
    # Create a list to append feature vectors to
    features = []
    # Iterate through the list of images
    for file in imgs:
        # Read in each one by one
        image = mpimg.imread(file)
        # apply color conversion if other than 'RGB'
        if cspace != 'RGB':
            if cspace == 'HSV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
            elif cspace == 'LUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
            elif cspace == 'HLS':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
            elif cspace == 'YUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
            elif cspace == 'YCrCb':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
        else: feature_image = np.copy(image)      

        # Call get_hog_features() with vis=False, feature_vec=True
        if hog_channel == 'ALL':
            hog_features = []
            for channel in range(feature_image.shape[2]):
                hog_features.append(get_hog_features(feature_image[:,:,channel], 
                                    orient, pix_per_cell, cell_per_block, 
                                    vis=False, feature_vec=True))
            hog_features = np.ravel(hog_features)        
        else:
            hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                        pix_per_cell, cell_per_block, vis=False, feature_vec=True)
        # Append the new feature vector to the features list
        features.append(hog_features)
    # Return list of feature vectors
    return features

Extract Features for Input Datasets and Combine, Define Labels Vector, Shuffle and Split

In [6]:
# Feature extraction parameters
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

t = time.time()
car_features = extract_features(car_images, cspace=colorspace, orient=orient, 
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                        hog_channel=hog_channel)
notcar_features = extract_features(noncar_images, cspace=colorspace, orient=orient, 
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                        hog_channel=hog_channel)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG features...')
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)  

# Fit a per-column scaler - this will be necessary if combining different types of features (HOG + color_hist/bin_spatial)
#X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
#scaled_X = X_scaler.transform(X)

# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))


# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=rand_state)

print('Using:',orient,'orientations',pix_per_cell,
    'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
40.37 Seconds to extract HOG features...
Using: 11 orientations 16 pixels per cell and 2 cells per block
Feature vector length: 1188

Train a Classifier

In [7]:
# Use a linear SVC 
svc = LinearSVC()
# Check the training time for the SVC
t = time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
0.96 Seconds to train SVC...
Test Accuracy of SVC =  0.9823
My SVC predicts:  [ 1.  0.  1.  0.  0.  1.  1.  0.  1.  0.]
For these 10 labels:  [ 1.  0.  1.  0.  0.  1.  1.  0.  1.  0.]
0.00188 Seconds to predict 10 labels with SVC

Method for Using Classifier to Detect Cars in an Image

In [8]:
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, cspace, hog_channel, svc, X_scaler, orient, 
              pix_per_cell, cell_per_block, spatial_size, hist_bins, show_all_rectangles=False):
    
    # array of rectangles where cars were detected
    rectangles = []
    
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]

    # apply color conversion if other than 'RGB'
    if cspace != 'RGB':
        if cspace == 'HSV':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)
        elif cspace == 'LUV':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)
        elif cspace == 'HLS':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)
        elif cspace == 'YUV':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
        elif cspace == 'YCrCb':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
    else: ctrans_tosearch = np.copy(image)   
    
    # rescale image if other than 1.0 scale
    if scale != 1:
        imshape = ctrans_tosearch.shape
        ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
    
    # select colorspace channel for HOG 
    if hog_channel == 'ALL':
        ch1 = ctrans_tosearch[:,:,0]
        ch2 = ctrans_tosearch[:,:,1]
        ch3 = ctrans_tosearch[:,:,2]
    else: 
        ch1 = ctrans_tosearch[:,:,hog_channel]

    # Define blocks and steps as above
    nxblocks = (ch1.shape[1] // pix_per_cell)+1  #-1
    nyblocks = (ch1.shape[0] // pix_per_cell)+1  #-1 
    nfeat_per_block = orient*cell_per_block**2
    # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
    window = 64
    nblocks_per_window = (window // pix_per_cell)-1 
    cells_per_step = 2  # Instead of overlap, define how many cells to step
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step
    
    # Compute individual channel HOG features for the entire image
    hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)   
    if hog_channel == 'ALL':
        hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
        hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
    
    for xb in range(nxsteps):
        for yb in range(nysteps):
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step
            # Extract HOG for this patch
            hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
            if hog_channel == 'ALL':
                hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
                hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
                hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
            else:
                hog_features = hog_feat1

            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell
            
            test_prediction = svc.predict(hog_features)
            
            if test_prediction == 1 or show_all_rectangles:
                xbox_left = np.int(xleft*scale)
                ytop_draw = np.int(ytop*scale)
                win_draw = np.int(window*scale)
                rectangles.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
                
    return rectangles

Test Find_Cars on an Example Image

In [9]:
test_img = mpimg.imread('./test_images/test1.jpg')

ystart = 400
ystop = 656
scale = 1.5
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

rectangles = find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, orient, pix_per_cell, cell_per_block, None, None)

print(len(rectangles), 'rectangles found in image')
plt.imshow(test_img)
6 rectangles found in image
Out[9]:
<matplotlib.image.AxesImage at 0x116c0cc18>

Method to Draw Rectangles onto an Image

In [10]:
# Here is your draw_boxes function from the previous exercise
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # Make a copy of the image
    imcopy = np.copy(img)
    random_color = False
    # Iterate through the bounding boxes
    for bbox in bboxes:
        if color == 'random' or random_color:
            color = (np.random.randint(0,255), np.random.randint(0,255), np.random.randint(0,255))
            random_color = True
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    # Return the image copy with boxes drawn
    return imcopy
In [11]:
test_img_rects = draw_boxes(test_img, rectangles)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
Out[11]:
<matplotlib.image.AxesImage at 0x11ae554e0>

Show All Potential Search Areas

In [36]:
test_img = mpimg.imread('./test_images/test1.jpg')

rects = []

ystart = 400
ystop = 464
scale = 1.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))
ystart = 416
ystop = 480
scale = 1.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  78
In [13]:
test_img = mpimg.imread('./test_images/test1.jpg')

rects = []

ystart = 400
ystop = 496
scale = 1.5
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))
ystart = 432
ystop = 528
scale = 1.5
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  50
In [14]:
test_img = mpimg.imread('./test_images/test1.jpg')

rects = []

ystart = 400
ystop = 528
scale = 2.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))
ystart = 432
ystop = 560
scale = 2.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  38
In [15]:
test_img = mpimg.imread('./test_images/test1.jpg')

rects = []

ystart = 400
ystop = 596
scale = 3.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))
ystart = 464
ystop = 660
scale = 3.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None, show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  24

Combine Various Sliding Window Searches

In [16]:
test_img = mpimg.imread('./test_images/test1.jpg')

rectangles = []

colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"


ystart = 400
ystop = 464
scale = 1.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 416
ystop = 480
scale = 1.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 400
ystop = 496
scale = 1.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 432
ystop = 528
scale = 1.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 400
ystop = 528
scale = 2.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 432
ystop = 560
scale = 2.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 400
ystop = 596
scale = 3.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))
ystart = 464
ystop = 660
scale = 3.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                       orient, pix_per_cell, cell_per_block, None, None))

# apparently this is the best way to flatten a list of lists
rectangles = [item for sublist in rectangles for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
Out[16]:
<matplotlib.image.AxesImage at 0x109a09438>

Heatmap

In [17]:
def add_heat(heatmap, bbox_list):
    # Iterate through list of bboxes
    for box in bbox_list:
        # Add += 1 for all pixels inside each bbox
        # Assuming each "box" takes the form ((x1, y1), (x2, y2))
        heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1

    # Return updated heatmap
    return heatmap
In [18]:
# Test out the heatmap
heatmap_img = np.zeros_like(test_img[:,:,0])
heatmap_img = add_heat(heatmap_img, rectangles)
plt.figure(figsize=(10,10))
plt.imshow(heatmap_img, cmap='hot')
Out[18]:
<matplotlib.image.AxesImage at 0x11e319b38>

Apply a Threshold to the Heatmap

In [19]:
def apply_threshold(heatmap, threshold):
    # Zero out pixels below the threshold
    heatmap[heatmap <= threshold] = 0
    # Return thresholded map
    return heatmap
In [20]:
heatmap_img = apply_threshold(heatmap_img, 1)
plt.figure(figsize=(10,10))
plt.imshow(heatmap_img, cmap='hot')
Out[20]:
<matplotlib.image.AxesImage at 0x11e1fab00>

Apply SciPy Labels to Heatmap

In [45]:
labels = label(heatmap_img)
plt.figure(figsize=(10,10))
plt.imshow(labels[0], cmap='gray')
print(labels[1], 'cars found')
2 cars found

Draw Bounding Boxes for Lables

In [22]:
def draw_labeled_bboxes(img, labels):
    # Iterate through all detected cars
    rects = []
    for car_number in range(1, labels[1]+1):
        # Find pixels with each car_number label value
        nonzero = (labels[0] == car_number).nonzero()
        # Identify x and y values of those pixels
        nonzeroy = np.array(nonzero[0])
        nonzerox = np.array(nonzero[1])
        # Define a bounding box based on min/max x and y
        bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
        rects.append(bbox)
        # Draw the box on the image
        cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
    # Return the image and final rectangles
    return img, rects

# Draw bounding boxes on a copy of the image
draw_img, rect = draw_labeled_bboxes(np.copy(test_img), labels)
# Display the image
plt.figure(figsize=(10,10))
plt.imshow(draw_img)
Out[22]:
<matplotlib.image.AxesImage at 0x11cf0e0f0>

Define a Class to Store Data from Vehicle Detections

In [23]:
# Define a class to store data from video
class Vehicle_Detect():
    def __init__(self):
        # history of rectangles previous n frames
        self.prev_rects = [] 
        
    def add_rects(self, rects):
        self.prev_rects.append(rects)
        if len(self.prev_rects) > 15:
            # throw out oldest rectangle set(s)
            self.prev_rects = self.prev_rects[len(self.prev_rects)-15:]

Define Pipeline

In [31]:
def process_frame(img):

    rectangles = []

    colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
    orient = 11
    pix_per_cell = 16
    cell_per_block = 2
    hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

    ystart = 400
    ystop = 464
    scale = 1.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 416
    ystop = 480
    scale = 1.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 400
    ystop = 496
    scale = 1.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 432
    ystop = 528
    scale = 1.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 400
    ystop = 528
    scale = 2.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 432
    ystop = 560
    scale = 2.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 400
    ystop = 596
    scale = 3.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 464
    ystop = 660
    scale = 3.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))

    rectangles = [item for sublist in rectangles for item in sublist] 
    
    heatmap_img = np.zeros_like(img[:,:,0])
    heatmap_img = add_heat(heatmap_img, rectangles)
    heatmap_img = apply_threshold(heatmap_img, 1)
    labels = label(heatmap_img)
    draw_img, rects = draw_labeled_bboxes(np.copy(img), labels)
    return draw_img
In [41]:
def process_frame_for_video(img):

    rectangles = []

    colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
    orient = 11
    pix_per_cell = 16
    cell_per_block = 2
    hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"
    
    ystart = 400
    ystop = 464
    scale = 1.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 416
    ystop = 480
    scale = 1.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 400
    ystop = 496
    scale = 1.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 432
    ystop = 528
    scale = 1.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 400
    ystop = 528
    scale = 2.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 432
    ystop = 560
    scale = 2.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 400
    ystop = 596
    scale = 3.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
    ystart = 464
    ystop = 660
    scale = 3.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, None, 
                           orient, pix_per_cell, cell_per_block, None, None))
 
    rectangles = [item for sublist in rectangles for item in sublist] 
    
    # add detections to the history
    if len(rectangles) > 0:
        det.add_rects(rectangles)
    
    heatmap_img = np.zeros_like(img[:,:,0])
    for rect_set in det.prev_rects:
        heatmap_img = add_heat(heatmap_img, rect_set)
    heatmap_img = apply_threshold(heatmap_img, 1 + len(det.prev_rects)//2)
     
    labels = label(heatmap_img)
    draw_img, rect = draw_labeled_bboxes(np.copy(img), labels)
    return draw_img

Run pipeline through test images

In [42]:
det = Vehicle_Detect()

test_images = glob.glob('./test_images/test*.jpg')

fig, axs = plt.subplots(3, 2, figsize=(16,14))
fig.subplots_adjust(hspace = .004, wspace=.002)
axs = axs.ravel()

for i, im in enumerate(test_images):
    axs[i].imshow(process_frame(mpimg.imread(im)))
    axs[i].axis('off')

Run video through pipeline

In [43]:
det = Vehicle_Detect()

test_out_file2 = 'test_video_out_2.mp4'
clip_test2 = VideoFileClip('test_video.mp4')
clip_test_out2 = clip_test2.fl_image(process_frame_for_video)
%time clip_test_out2.write_videofile(test_out_file2, audio=False)
[MoviePy] >>>> Building video test_video_out_2.mp4
[MoviePy] Writing video test_video_out_2.mp4
 97%|█████████▋| 38/39 [00:09<00:00,  4.20it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: test_video_out_2.mp4 

CPU times: user 8.17 s, sys: 1.69 s, total: 9.87 s
Wall time: 10.2 s
In [44]:
det = Vehicle_Detect()

proj_out_file = 'project_video_out.mp4'
clip_proj = VideoFileClip('project_video.mp4')
clip_proj_out = clip_proj.fl_image(process_frame_for_video)
%time clip_proj_out.write_videofile(proj_out_file, audio=False)
[MoviePy] >>>> Building video project_video_out.mp4
[MoviePy] Writing video project_video_out.mp4
100%|█████████▉| 1260/1261 [04:58<00:00,  4.28it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: project_video_out.mp4 

CPU times: user 4min 24s, sys: 49.5 s, total: 5min 13s
Wall time: 4min 59s
In [ ]: